CN115509115A - Fuzzy self-adaptive PID control-based magnetic nanoparticle heat generation optimization method - Google Patents
Fuzzy self-adaptive PID control-based magnetic nanoparticle heat generation optimization method Download PDFInfo
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- 239000002122 magnetic nanoparticle Substances 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000005457 optimization Methods 0.000 title claims abstract description 13
- 230000020169 heat generation Effects 0.000 title claims description 15
- 238000009826 distribution Methods 0.000 claims abstract description 12
- 238000010438 heat treatment Methods 0.000 claims abstract description 12
- 238000012546 transfer Methods 0.000 claims abstract description 11
- 230000009471 action Effects 0.000 claims abstract description 7
- 230000009191 jumping Effects 0.000 claims abstract description 4
- 230000003044 adaptive effect Effects 0.000 claims description 15
- 239000008280 blood Substances 0.000 claims description 9
- 210000004369 blood Anatomy 0.000 claims description 9
- 230000006870 function Effects 0.000 claims description 6
- 230000008081 blood perfusion Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 238000009795 derivation Methods 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 230000002503 metabolic effect Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract 1
- 206010020843 Hyperthermia Diseases 0.000 description 3
- 238000002679 ablation Methods 0.000 description 3
- 230000036031 hyperthermia Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
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- 230000008685 targeting Effects 0.000 description 1
- 238000000015 thermotherapy Methods 0.000 description 1
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N2/00—Magnetotherapy
- A61N2/02—Magnetotherapy using magnetic fields produced by coils, including single turn loops or electromagnets
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Abstract
The invention relates to a fuzzy self-adaptive PID control-based magnetic nanoparticle heat production optimization method, which comprises the following steps of: step S1: constructing a geometric model of the biological tissue; step S2: constructing a Pennes biological heat transfer model based on a geometric model of the biological tissue, and predicting the temperature distribution in the biological tissue; and step S3: controlling the heating power of the alternating magnetic field through a PID algorithm; and step S4: controlling the heating power of the alternating magnetic field based on the PID, judging whether the highest temperature of the area is converged to a set value, if not, setting the parameters of the PID algorithm by using a fuzzy controller, and if so, jumping to the step S5; step S5: and using the set parameters in a PID algorithm and outputting an optimized temperature control curve. The invention realizes the control of the calorific value of the magnetic nanoparticles under the action of the alternating magnetic field through the fuzzy self-adaptive PID, thereby effectively controlling the temperature distribution of the tissue area.
Description
Technical Field
The invention relates to the technical field of modeling of magnetic nanoparticles in an alternating magnetic field, in particular to a fuzzy self-adaptive PID control-based magnetic nanoparticle heat generation optimization method.
Background
The emerging technology is continuously developed along with the development of science and technology, and in recent years, the magnetic nano thermotherapy becomes a potential tissue thermal ablation technology with the advantages of high safety, few side effects, high targeting and the like. Under the action of the alternating magnetic field, the magnetic nano particles can convert magnetic field energy into heat energy, so that the temperature of a target area is increased to 42-46 ℃, and the purpose of local tissue ablation is achieved.
In magnetic nano hyperthermia, it is desirable to maintain the temperature of the target area in the range of 42-46 ℃ so that the temperature in the tissue can kill specific cells without damaging normal tissue. Therefore, the heat generation of the magnetic nanoparticles needs to be controlled and optimized in the magnetic nano hyperthermia, so that the accuracy and stability of the treatment temperature in the magnetic nano hyperthermia are improved.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for optimizing heat generation of magnetic nanoparticles based on fuzzy adaptive PID control, which realizes controlling the heat generation value of magnetic nanoparticles under the action of an alternating magnetic field by fuzzy adaptive PID, and further effectively controls the temperature distribution of a tissue region.
In order to achieve the purpose, the invention adopts the following technical scheme:
a magnetic nanoparticle heat generation optimization method based on fuzzy self-adaptive PID control comprises the following steps:
step S1: constructing a geometric model of the biological tissue;
step S2: constructing a Pennes biological heat transfer model based on a geometric model of the biological tissue, and predicting the temperature distribution in the biological tissue;
and step S3: controlling the heating power of the alternating magnetic field through a PID algorithm;
and step S4: controlling the heating power of the alternating magnetic field based on the PID, judging whether the highest temperature of the area is converged to a set value, if not, setting the parameters of the PID algorithm by using a fuzzy controller, and if so, jumping to the step S5;
step S5: and using the set parameters in a PID algorithm and outputting an optimized temperature control curve.
Further, the geometric model of the biological tissue is composed of a circle and an ellipse, wherein the radius is R 1 The circle of (a) represents a first tissue region, and the ellipse having a major axis a and a minor axis b represents a second tissue region, wherein the first tissue region is contained in the second tissue region.
Further, the Pennes biological heat transfer model is as follows:
wherein ρ, c, T and k represent the density, specific heat capacity, absolute temperature and heat conductivity coefficient of the tissue, ω b 、ρ b 、c b 、T b Respectively representing blood perfusion rate, blood density, blood specific heat capacity and blood temperature, t represents heating time of the magnetic nanoparticles under the action of an alternating magnetic field, and symbols
Representing Hamiltonian, Q m Represents the metabolic heat per unit volume, alpha represents the correction factor for power dissipation, and P represents power dissipation.
Further, the predicting the temperature distribution in the biological tissue specifically includes: setting attribute parameters of the tissue region, and solving the Pennes biological heat transfer model by adopting a finite element method, wherein the tissue region parameters comprise the density, specific heat capacity and heat conductivity coefficient of the tissue.
Further, the control equation of the PID algorithm is:
error(t)＝y d (t)-y(t)
wherein error (t) represents the set temperature y d The difference between (t) and the actual output temperature y (t), u (t) represents the PID control equation, k p Denotes the proportionality coefficient, k i Denotes the integration time constant, k d Representing the differential time constant.
Further, the step S4 specifically includes the following steps:
step S41: initial parameter k for a given PID control p 、k i 、k d ；
Step S42: derivation of error and error
Step S43: determining input membership functions, derivatives of error and error
Fuzzification is carried out;
step S44: determining fuzzy rule for parameter k p 、k i 、k d Fuzzy setting is carried out;
step S45: determining an output membership function, and deblurring the output fuzzy quantity to obtain an accurate quantity;
step S46: will k p 、k i 、k d The initial value of the PID controller and the accurate value obtained in the step S45 are linearly combined to form a new controlled variable of the PID equation, the new controlled variable is brought back to the PID control equation, and whether the temperature is converged to a set value or not is judged; if the temperature does not converge to the set point, the above steps need to be repeated until the temperature converges to the set point.
Further, the fuzzification is to describe the error and the magnitude of the derivative of the error through a language form, and the fuzzy subset is as follows: { NB, NM, NS, ZO, PS, PM, PB }, representing { big negative, middle negative, small negative, zero, small positive, middle positive, big positive }, respectively.
Further, the defuzzification adopts a gravity center method to solve fuzzy output, and the expression is as follows:
wherein,z 0 representing the exact value, mu, of the output of the fuzzy controller after the output is deblurred c (z i ) Denotes z i Of membership value of z i Are values in the fuzzy control theoretic domain.
Compared with the prior art, the invention has the following beneficial effects:
the invention realizes the control of the calorific value of the magnetic nanoparticles under the action of the alternating magnetic field through the fuzzy self-adaptive PID, and further effectively controls the temperature distribution of the tissue area, thereby effectively improving the effect of tissue thermal ablation and improving the control precision.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a geometric model constructed in an embodiment of the present invention
FIG. 3 is a schematic diagram of temperature distribution prediction without fuzzy adaptive PID control according to an embodiment of the present invention
FIG. 4 is a temperature profile without fuzzy adaptive PID control in accordance with an embodiment of the invention;
FIG. 5 is a temperature profile of fuzzy adaptive PID control in one embodiment of the invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the invention provides a fuzzy adaptive PID control-based magnetic nanoparticle heat generation optimization method, which includes the following steps:
step S1: constructing a geometric model of the biological tissue;
step S2: constructing a Pennes biological heat transfer model based on a geometric model of the biological tissue, and predicting the temperature distribution in the biological tissue;
and step S3: controlling the heating power of the alternating magnetic field through a PID algorithm;
and step S4: controlling the heating power of the alternating magnetic field based on the PID, judging whether the highest temperature of the area is converged to a set value, if not, setting the parameters of the PID algorithm by using a fuzzy controller, and if so, jumping to the step S5;
step S5: and using the set parameters in a PID algorithm and outputting an optimized temperature control curve.
Preferably, in this embodiment, the geometric model described in step S1 is shown in fig. 2, and the model is composed of a circle and an ellipse, wherein the radius is R 1 A circle of =5mm represents a first tissue area, and an ellipse having a major axis of a =20mm and a minor axis of b =15mm represents a second tissue area, wherein the first tissue area is included in the second tissue area.
Preferably, in this embodiment, in step S2, a Pennes biological heat transfer model is constructed to predict the temperature distribution in the biological tissue, where the Pennes biological heat transfer model is:
wherein ρ, c, T and k represent the density, specific heat capacity, absolute temperature and heat conductivity coefficient of the tissue, ω b 、ρ b 、c b 、T b Respectively representing blood perfusion rate, blood density, blood specific heat capacity and blood temperature, t represents heating time of the magnetic nanoparticles under the action of an alternating magnetic field, and symbols
Representing Hamiltonian, Q m Represents the metabolic heat per unit volume, alpha represents the correction factor for power dissipation, and P represents power dissipation.
Preferably, in this embodiment, the setting of the property parameters of the tissue region and the solving of the biological heat transfer equation by using the finite element method are performed, and the temperature distribution in the tissue is as shown in fig. 3, wherein the tissue region parameters include the density, specific heat capacity and thermal conductivity of the tissue. The parameter data corresponding to the first tissue region are: 1060Kg · m -3 、3540J·Kg -1 ·K -1 、0.52W·m -1 · K -1 The parameter data corresponding to the second tissue region is: 1064Kg m -3 、4500J·Kg -1 · K -1 、0.59W·m -1 ·K -1 。
Preferably, in this embodiment, in the step S3, the alternating magnetic field heating power is controlled by a PID algorithm, and a control equation of the PID algorithm is error (t) = y d (t)-y(t)
Wherein error (t) represents the set temperature y d The difference between (t) and the actual output temperature y (t), u (t) represents the PID control equation, k p Denotes the proportionality coefficient, k i Denotes the integration time constant, k d Representing the differential time constant.
Preferably, in this embodiment, step S41: initial parameter k for a given PID control p ＝ 1、k i ＝0.05、k d ＝0.05；
Step S42: derivation of error and error
Step S43: determining input membership functions, derivatives of error and error
Fuzzification is carried out;
step S44: determining fuzzy rule for parameter k p 、k i 、k d Fuzzy setting is carried out;
step S45: determining an output membership function, and deblurring the output fuzzy quantity to obtain an accurate quantity;
step S46: will k p 、k i 、k d The initial value of the PID controller and the accurate value obtained in step S45 are linearly combined to form a new controlled variable of the PID equation, and the new controlled variable is brought back to the PID control equation to judge whether the temperature is converged to the set value. If the temperature does not converge to 46 deg.C, the above steps need to be repeated until the temperature converges to the set point. FIG. 4 is a temperature profile with center point not being fuzzy adaptive PID controlled, FIG. 5 is a center pointPoint-fuzzy adaptive PID controlled temperature profile.
Preferably, in this embodiment, the step S4 of blurring is to describe the error and the magnitude of the derivative of the error by a language, and the subset of blurring is: { NB, NM, NS, ZO, PS, PM, PB }, representing { big negative, middle negative, small negative, zero, small positive, middle positive, big positive }, respectively.
Preferably, in this embodiment, the defuzzification in step S4 uses a gravity center method to solve the fuzzy output, and the expression is:
wherein z is 0 Representing the exact value, mu, of the output of the fuzzy controller after the output is deblurred c (z i ) Denotes z i Of membership value of z i Are values in the fuzzy control theoretic domain.
Preferably, in this embodiment, in the step S5, the setting k in the step S4 is adjusted p 、 k i 、k d And the temperature control curve is used in a PID algorithm and the optimized temperature control curve is output.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (8)
1. A magnetic nanoparticle heat generation optimization method based on fuzzy self-adaptive PID control is characterized by comprising the following steps:
step S1: constructing a geometric model of the biological tissue;
step S2: constructing a Pennes biological heat transfer model based on a geometric model of the biological tissue, and predicting the temperature distribution in the biological tissue;
and step S3: controlling the heating power of the alternating magnetic field through a PID algorithm;
and step S4: controlling the heating power of the alternating magnetic field based on the PID, judging whether the highest temperature of the area is converged to a set value, if not, setting the parameters of the PID algorithm by using a fuzzy controller, and if so, jumping to the step S5;
step S5: and using the set parameters in a PID algorithm and outputting an optimized temperature control curve.
2. The method for optimizing the generation of heat of magnetic nanoparticles based on fuzzy adaptive PID control as claimed in claim 1, wherein the geometric model of the biological tissue is composed of a circle and an ellipse, wherein the radius is R 1 The circle of (b) represents a first tissue area, and the ellipse with major axis a and minor axis b represents a second tissue area, wherein the first tissue area is contained in the second tissue area.
3. The magnetic nanoparticle heat generation optimization method based on the fuzzy adaptive PID control according to claim 1, wherein the Pennes biological heat transfer model is:
wherein ρ, c, T and k represent the density, specific heat capacity, absolute temperature and heat conductivity coefficient of the tissue, ω b 、ρ b 、c b 、T b Respectively representing blood perfusion rate, blood density, blood specific heat capacity and blood temperature, t represents heating time of the magnetic nanoparticles under the action of an alternating magnetic field, and symbol
Representing Hamiltonian, Q m Represents the metabolic heat per unit volume, alpha represents the correction factor for power dissipation, and P represents power dissipation.
4. The fuzzy adaptive PID control based magnetic nanoparticle heat generation optimization method according to claim 3, wherein the predicting of temperature distribution in biological tissue is specifically: setting attribute parameters of the tissue region, and solving the Pennes biological heat transfer model by adopting a finite element method, wherein the tissue region parameters comprise the density, specific heat capacity and heat conductivity coefficient of the tissue.
5. The fuzzy adaptive PID control based magnetic nanoparticle heat generation optimization method according to claim 1, wherein the control equation of the PID algorithm is:
error(t)＝y d (t)-y(t)
wherein error (t) represents the set temperature y d The difference between (t) and the actual output temperature y (t), u (t) represents the PID control equation, k p Denotes the proportionality coefficient, k i Denotes the integration time constant, k d Representing the differential time constant.
6. The fuzzy adaptive PID control based magnetic nanoparticle heat generation optimization method according to claim 1, wherein the step S4 specifically comprises the following steps:
step S41: initial parameter k for a given PID control p 、k i 、k d ；
Step S42: derivation of error and error
Step S43: determining input membership functions, derivatives of error and error
Fuzzification is carried out;
step S44: determining fuzzy rule for parameter k p 、k i 、k d Fuzzy setting is carried out;
step S45: determining an output membership function, and deblurring the output fuzzy quantity to obtain an accurate quantity;
step S46: will k p 、k i 、k d The initial value of the PID controller and the accurate value obtained in the step S45 are linearly combined to form a new controlled variable of the PID equation, the new controlled variable is brought back to the PID control equation, and whether the temperature is converged to a set value or not is judged; if the temperature does not converge to the set point, the above steps need to be repeated until the temperature converges to the set point.
7. The fuzzy adaptive PID control based magnetic nanoparticle heat generation optimization method of claim 6, wherein the fuzzification is to describe the error and the derivative of the error in terms of language, and the fuzzy subset is: { NB, NM, NS, ZO, PS, PM, PB }, representing { big negative, middle negative, small negative, zero, small positive, middle positive, big positive }, respectively.
8. The fuzzy adaptive PID control based magnetic nanoparticle heat generation optimization method of claim 6, wherein the defuzzification uses a gravity center method to solve fuzzy output, and the expression is as follows:
wherein z is 0 Representing the exact value, mu, of the output of the fuzzy controller after the output has been deblurred c (z i ) Denotes z i Degree of membership of z i Are values in the fuzzy control theoretic domain.
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| CN112947091A (en) * | 2021-03-26 | 2021-06-11 | 福州大学 | PID control-based method for optimizing heat production of magnetic nanoparticles in biological tissues |
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